From Disruption to Design: Educator-Led Generative AI Learning Interventions in Marketing Education

Quamina, LT, Ghandour, R ORCID: 0000-0002-2284-0671, Hardley, F and Baig, A (2026) From Disruption to Design: Educator-Led Generative AI Learning Interventions in Marketing Education. Marketing Education Review. ISSN 1052-8008

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Abstract

Generative Artificial Intelligence (GenAI) is transforming marketing practice and in turn marketing education. Yet educators lack empirical guidance on how best to implement GenAI pedagogically in the classroom. This study addresses that gap by examining how educator-led AI interventions, AI-Directed, AI-Supported, and AI-Empowered affect marketing students’ motivation, engagement, satisfaction, and intentions to learn. Drawing on Agency theory and employing a between-subjects experimental design (n = 160), we find that AI-empowered interventions in which educators design open-ended tasks that balance structure with autonomy, significantly enhance motivation, engagement, and learning intentions. Our findings move beyond conceptual debates to offer empirically grounded, theory-informed recommendations for integrating GenAI into teaching practice. We argue for a reorientation toward theory-informed pedagogies that position GenAI as a means to foster agency not automation. Crucially, we recognize that educators play a central role in mediating GenAI’s pedagogical relevance in marketing education.

Item Type:	Article
Uncontrolled Keywords:	35 Commerce, Management, Tourism and Services; 3506 Marketing; Machine Learning and Artificial Intelligence; Clinical Research; 4 Quality Education; 1505 Marketing; 3506 Marketing
Subjects:	H Social Sciences > HF Commerce > HF5001 Business L Education > L Education (General) H Social Sciences > HF Commerce > HF5001 Business > HF5410 Marketing. Distribution of Products Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Liverpool Business School
Publisher:	Informa UK Limited
Date of acceptance:	8 July 2026
Date of first compliant Open Access:	25 August 2026
Date Deposited:	25 Aug 2026 11:37
Last Modified:	25 Aug 2026 12:30
DOI or ID number:	10.1080/10528008.2026.2703575
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29194

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